Detecting CNN-generated Images. In this paragraph, we provide a brief summary of existing methods for detection of CNN-generated images. Inspired by solutions in steganalysis,
Cozzolino [1]} proposed a CNN architecture that
mimics the rich models in feature extraction and classification. Zhang et al. [2]} proposed to feed spectral input (rather than pixel input) to a classifier. They also introduced a GAN
simulator, called AutoGAN, that simulates artifacts produced by popular GAN models. Recently,
Wang et al. [3]} trained a
classifier with a large number of ProGAN-generated images and evaluated it on images synthesized by eleven different generators. Their work
showed the effectiveness of extensive data augmentation in improving the generalization ability of a classifier.
